Iris recognition technologies for identity management are already deployed globally in several large-scale nationwide biometric projects and are currently entering the mobile market. More recently, periocular recognition has been employed to augment the biometric performance of the iris in unconstrained environments where only the ocular region is present in the image. Iris and Periocular Biometric Recognition provides an overview of scientific fundamentals and principles of iris and periocular biometric recognition over six broad areas: an introduction to iris and periocular recognition; a selective overview of issues and challenges; soft biometric classification; security aspects; privacy protection and forensics; and future trends. With contributions from experts in industry and academia, this book is essential reading for researchers, graduate students and practitioners in biometrics and related fields.

Part I. Introduction to Iris and Periocular Recognition

The rich random pattern visible in the iris of the eye represents one of the strongest biometric characteristics and considerable research efforts are being invested to push forward iris recognition technologies. Numerous covariates of iris recognition are the focus of current research programmes, which aim at improving different key factors of iris biometric system, e.g., usability, acceptability or interoperability. This chapter provides a very brief overview of the processing chain of generic iris recognition systems as well as a the current state-of-the-art. Furthermore, current challenges in the field of iris recognition were touched upon.

Periocular biometrics specifically refers to the externally visible skin region of the face that surrounds the eye socket. Its utility is specially pronounced when the iris or the face cannot be properly acquired, being the ocular modality requiring the least constrained acquisition process. It appears over a wide range of distances, even under partial face occlusion (close distance) or low resolution iris (long distance), making it very suitable for unconstrained or uncooperative scenarios. It also avoids the need of iris segmentation, an issue in difficult images. In such situation, identifying a suspect where only the periocular region is visible is one of the toughest real-world challenges in biometrics. The richness of the periocular region in terms of identity is so high that the whole face can even be reconstructed only from images of the periocular region. The technological shift to mobile devices has also resulted in many identity-sensitive applications becoming prevalent on these devices.

Part II. Issues and Challenges

In this chapter we presented current trends in iris segmentation, summarising the advances in state-of-the-art individual segmentation, shedding light on the pitfalls of NIR vs. VIS iris segmentation and illustrating means to tune existing iris segmentation towards specific datasets. We highlighted different forms of segmentation accuracy assessment and evaluated different approaches to combine segmentation algorithms. To get more stable results we integrated segmentation quality prediction in the fusion process.Tests on ground-truth confirm this positive effect across the board. For the goal of developing more robust iris segmentation techniques, multi-segmentation fusion and quality prediction can be a versatile tool to obtain more stable and more accurate results. However, performing fusion and running multiple segmentation algorithms impacts the processing time. This was not explicitly covered and further research on frame throughput might be necessary for video-based iris segmentation.

Linear combinations of metrics for assessing biometric sample quality are weak, because they lack veto power. For example, a good score for a sharp focus of an ocular image would `compensate' in an additive combination for the fact that the eyelids are fully closed; or fully open eyelids would compensate for the image being many diopters out-of-focus. Normalised multiplicative quality factors are better because they are punitive, and thereby confer veto powers. This chapter explains the basis for the product of power functions which underlie the ISO/IEC 29794-6 Iris Image Sample Quality Standard, in particular how the exponents of the power functions allow importance tailoring of each element.

Indexing/retrieving sets of iris biometric signatures has been a topic of increasing popularity, mostly due to the deployment of iris recognition systems in nationwide scale scenarios. In these conditions, for each identification attempt, there might exist hundreds of millions of enrolled identities and is unrealistic to match the probe against all gallery elements in a reasonable amount of time. Hence, the idea of indexing/retrieval is - upon receiving one sample - to find in a quick way a subset of elements in the database that most probably contains the identity of interest, i.e., the one corresponding to the probe. Most of the state-of-the-art strategies to index iris biometric signatures were devised to decision environments with a clear separation between genuine and impostor matching scores. However, if iris recognition systems work in low quality data, the resulting decision environments are poorly separable, with a significant overlap between the distributions of both matching scores. This chapter summarizes the state-of-the-art in terms of iris biometric indexing/retrieval and focuses in an indexing/retrieval method for such low quality data and operates at the code level, i.e., after the signature encoding process. Gallery codes are decomposed at multiple scales, and using the most reliable components of each scale, their position in a n-ary tree is determined. During retrieval, the probe is decomposed similarly, and the distances to multi-scale centroids are used to penalize paths in the tree. At the end, only a subset of branches is traversed up to the last level.

Periocular images contain iris as well as other near-by regions such as eyelids and eyebrows. It is useful to know which regions of ocular images are important for achieving good recognition performance. This chapter will present observations from experiments with regard to the effect of changing the size or selection of the periocular region on biometric recognition performance. One can use a single definition of the periocular region to identify areas that contain significant discriminative textural information in an attempt to identify such regions. In this chapter, we investigate sub-region effects over varying cropping sizes to determine an appropriate periocular image representation.

In this chapter, a new paradigm is presented for solving the problem of out-of-focus imaging in visible spectrum iris recognition by employing a Light Field Camera (LFC). The ability of LFC, to refocus the parts of interest in a scene after the image capture, is explored to solve the out-of-focus problem in iris imaging in visible spectrum. This chapter also extends the use of LFC for periocular recognition and thereby presents an ocular biometric system using LFC in visible spectrum. Further, the chapter also presents the weighted score level fusion of iris and periocular characteristics to improve the overall performance.

Part III. Soft Biometric Classification

Gender classification is an important topic in a wide variety of applications ranging from surveillance to selective marketing. Several recent studies have shown the predominance of local matching approaches in gender classifications results. Previous works in predicting gender-from-iris have relied on computing a separate set of textures representation. The state of the art shows that gender can be successfully predicted from the iris. There are clear computational advantages to predicting gender from the binary iris-code rather than computing another different texture representation. This topic brings new insights about the information present in the iris (and iris-code) to determine demographic information. The previous work adds evidence answering the fundamental question that the iris contains specific information about us, such as gender. The results, which show that gender classification from iris code is possible, will spur research to determine if other demographic factors (e.g., ethnicity, age, emotions) can also be predicted. This is an area of research that is overall in the early stages.

Soft biometric traits refer to characteristics that provide some information about an individual but do not possess the distinctiveness and permanence necessary to sufficiently differentiate between any two individuals. Examples of soft biometric traits include gender, ethnicity, age, weight, and height. As early as 1997, researchers suggested that soft biometrics could be used to improve biometric recognition performance [1]. Researchers later demonstrated the use of gender, ethnicity, and height to improve the performance of a fingerprint recognition system [2]. This chapter discusses the use of local appearance features extracted from the periocular region for gender and ethnicity classification.

As biometrics-based identity authentication systems have become more widely deployed, it has become evident that traditional identification and verification tasks are not the only application for such approaches. The prediction of individual, but non-unique, characteristics such as subject age is also an obvious option, since there are diverse situations in which information short of absolute identity is itself valuable. Physical ageing is an important issue for practical biometrics, since it is known that the associated physiological changes can impair performance for most modalities. Understanding the effects of ageing is necessary, therefore, both to optimise attainable performance but also to understand how to manage biometric templates, especially as the time elapsed between enrolment and use increases. Age prediction is relatively poorly represented in the literature. This chapter will explore applications of age prediction from iris biometrics and the implications for the underpinning computational structures.

Part IV. Security Aspects

In the present chapter, multibiometric techniques are included in this category. Multibiometric anti-spoofing is based on the hypothesis that the combination of different biometrics will increase the robustness to direct attacks, as, in theory, generating several fake traits is presumed to be more difficult than an individual trait. Such a strategy requires additional hardware acquisition devices, therefore these techniques may be included in the sensor-level group of presentation attack detection methods.

Iris texture provides the means for extremely accurate uni-modal person identification. However, the accuracy of iris-based biometric systems is sensitive to the presence of contact lenses in acquired sample images. This is especially true in the case of textured (cosmetic) contact lenses that can be effectively used to obscure the original iris texture of a subject and consequently to perform presentation attacks. Since also transparent contact lenses can degrade matching rates, automatic detection and classification of different contact lens types is needed in order to improve the robustness of iris-based biometric systems. This chapter introduces the problem of contact lens detection with particular focus on cosmetic contact lenses. The state of the art is analysed thoroughly and a case study on generalised textured contact lens detection is provided. The potential future research directions are also discussed.

The wide deployment of biometric systems over the last few years has motivated an increased amount of research on both vulnerabilities ofbiometric schemes to software attacks and the development of appropriate countermeasures. The focus is not limited any more to decreasing error rates or verification time, but it is slowly moving towards the analysis of the security and privacy granted by biometric systems. The works summarized in this chapter have shown that even the biometric characteristics considered the most secure due to its inherent stochastic nature, such as the iris, are vulnerable to a non-negligible number of external attacks. In an attempt to reduce such vulnerabilities, some countermeasures have been proposed.

Part V. Privacy Protection and Forensics

This chapter provides a summary of iris biometric template protection schemes. A review and discussion of state-of-the-art approaches is presented, based on which an outlook to future prospects is given. In addition, we provide a detailed insight into the construction of two prominent iris biometric template protection schemes in particular, the fuzzy vault scheme and the bin-combo scheme. Both schemes are evaluated and compared on a publicly available iris database with respect to provided privacy protection and biometric performance.

In this chapter, we describe how to perform a secure and efficient IrisCode-based identification. In this use case, the first party has an IrisCode, the second party has one or several IrisCodes and they would like to discover whether the first party's IrisCode is close to at least one IrisCode belonging to the second party without revealing any information about their own IrisCodes to the opposing party. Secure Two-Party Computation (S2PC) protocols are dedicated to this use case because they enable two parties to jointly evaluate a function over their inputs while preserving the privacy of their inputs. In this chapter, we explain how to efficiently use S2PC protocols for secure iris-based identification.

In this chapter we examined two passive approaches to secure an iris recognition system against insertion attacks by verifying the authenticity of the iris images. The first one, named PSI, is based on the photo response non-uniformity (PRNU) of image sensors and the second one, named ITC, exploits the texture information of unrolled iris images. The examination was performed using images from nine distinct iris databases or sensors, respectively

In this chapter, the problem of matching face against iris images using ocular information is considered. Face and iris images are typically acquired using different sensors: face recognition is predominantly conducted in the visible (VIS) spectrum while iris recognition is performed in the near-infrared (NIR) spectrum. Further, the subject-to-camera distance for face and iris recognition is substantially different. Due to these and other factors, the problem of matching face images against iris images is riddled with several challenges. To address this, we propose a novel matching algorithm based on Joint Dictionary-based Sparse Representation (JDSR) that exploits the use of ocular information available in both face and iris images. Experimental results on a database containing 1,358 images of 704 subjects indicate that the ocular region can provide better performance than the iris region in this challenging cross-modality matching scenario.

Part VI. Future Trends

Embedded systems are becoming widespread nowadays. We can find these systems in many applications, as part of bigger systems but as well as standalone systems. Designed for the increasing necessity of access control, many implementations of iris biometrics systems are being carried out in embedded systems. This chapter is focused on such developments, and therefore is structured as follows: first an introduction to embedded systems is made, to later describe some of the most common architectures of these systems and the design alternatives. After that, considering the particular case of an iris biometric system, detailed requirements are described. These requirements are divided into functionality and security requirements. Later on, state-of-the-art implementations according to different design alternatives are presented.

An environment is now a place that can offer services utilizing information and communication technology (ICT) in various scenes of daily life, and a wide range of operations and commercial transactions are becoming cloud-based. In this situation, biometric authentication is becoming widespread as a reliable and simple means of user authentication. Fujitsu started providing biometric authentication devices for PCs in 1999. Subsequently, we have worked on the development of biometric authentication technologies for notebook PCs and smartphones, pursuing convenience as well as security. This paper presents Fujitsu's activities related to biometric authentication technologies, centering on the successful integration of iris authentication in a smartphone for the first time in the world.

Since 1994 iris recognition was established as a state-of-the-art technology in the field of biometric recognition, and after central intellectual property rights expired in 2011 it was established as a reliable alternative to fingerprint and face recognition-based systems. Its relevance is further backed by the integration in the currently largest biometric project (UID) as one of the main biometric modalities. In this chapter we discuss the current state, pending challenges, and upcoming trends of iris recognition from an industry perspective.